Abstract

Many applications generate and/or consume multi-variate temporal data, yet experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this paper, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time-series along with external knowledge, including variate relationships, known a priori. Relying on these observations, we develop algorithms to detect robust multi-variate temporal (RMT) features which can be indexed for efficient and accurate retrieval and can be used for supporting analysis tasks, such as classification. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.

abstract = "Many applications generate and/or consume multi-variate temporal data, yet experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this paper, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time-series along with external knowledge, including variate relationships, known a priori. Relying on these observations, we develop algorithms to detect robust multi-variate temporal (RMT) features which can be indexed for efficient and accurate retrieval and can be used for supporting analysis tasks, such as classification. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.",

N2 - Many applications generate and/or consume multi-variate temporal data, yet experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this paper, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time-series along with external knowledge, including variate relationships, known a priori. Relying on these observations, we develop algorithms to detect robust multi-variate temporal (RMT) features which can be indexed for efficient and accurate retrieval and can be used for supporting analysis tasks, such as classification. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.

AB - Many applications generate and/or consume multi-variate temporal data, yet experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this paper, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time-series along with external knowledge, including variate relationships, known a priori. Relying on these observations, we develop algorithms to detect robust multi-variate temporal (RMT) features which can be indexed for efficient and accurate retrieval and can be used for supporting analysis tasks, such as classification. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.